Deploy OpenClaw on AWS EC2 and EKS, Azure Virtual Machines and AKS, and Google Cloud Compute Engine and GKE. Includes detailed cost breakdowns per cloud, GPU instance sizing for simulation, and architecture patterns for hybrid cloud-to-robot connectivity.
Launch an EC2 instance with Ubuntu 22.04 LTS from the AMI marketplace. Choose instance type g4dn.xlarge for GPU simulation or t3.large for CPU-only workloads. In the Security Group allow inbound TCP 7400 (OpenClaw API) and TCP 22 (SSH). SSH into the instance and install: curl -fsSL https://apt.openclaw.dev/key.gpg | sudo gpg --dearmor -o /etc/apt/keyrings/openclaw.gpg && echo "deb [arch=amd64 signed-by=/etc/apt/keyrings/openclaw.gpg] https://apt.openclaw.dev stable main" | sudo tee /etc/apt/sources.list.d/openclaw.list && sudo apt update && sudo apt install openclaw.
For a development team running continuous GPU simulation: g4dn.xlarge on-demand at $0.526/hour = $378/month per instance. With a 1-year Reserved Instance the cost drops to ~$225/month. For CPU-only pipeline development on t3.large at $0.0832/hour = $60/month. For production team running 8-hour simulation workdays: g4dn.xlarge reserved = ~$60/month effective. Data transfer costs add $0.09/GB outbound beyond the free tier.
Create an Azure Virtual Machine with Ubuntu 22.04. Choose Standard_NV6ads_A10_v5 for GPU workloads or Standard_D4s_v5 for CPU-only use. Open inbound port 7400 in the Network Security Group. Connect via SSH and install OpenClaw using the same APT repository commands as Ubuntu. For AKS deployment, use the openclaw Helm chart with the Azure-managed identity annotation for Key Vault secret access.
Standard_NV6ads_A10_v5 on-demand: $0.45/hour = $324/month. With Azure Reserved Virtual Machine Instance (1-year): ~$195/month. Standard_D4s_v5 (4 vCPU, 16 GB, CPU-only): $0.192/hour = $138/month. With savings plan: ~$110/month. Azure Spot Instances for non-critical simulation: up to 80% discount = as low as $65/month for GPU workloads on interruptible spot instances.
Create a GCP Compute Engine VM with Ubuntu 22.04 in us-central1. Choose n2-standard-4 machine type and attach an NVIDIA T4 GPU accelerator. Allow TCP 7400 in the firewall rules. Connect via gcloud compute ssh and install OpenClaw via the APT repository. For GKE: install the openclaw Helm chart and configure Workload Identity for service account binding to GCS log buckets.
n2-standard-4 + NVIDIA T4 GPU on-demand in us-central1: ~$0.38/hour = $274/month. With 1-year committed use discount: ~$190/month. For CPU-only n2-standard-4 without GPU: $0.19/hour = $137/month. Google Cloud Spot VMs for non-critical batch simulation: ~$0.11/hour = $79/month. Sustained use discounts apply automatically after 25% monthly usage, reducing effective hourly rates by up to 30%.
Based on GPU instance pricing for equivalent compute: GCP is typically 10 to 15 percent cheaper than AWS and 5 to 10 percent cheaper than Azure for sustained workloads. However, factor in egress costs (all three charge ~$0.08-0.09/GB outbound), storage, and support tiers. AWS has the widest instance variety; Azure integrates best with Windows-based enterprise tooling; GCP offers the best sustained use discounts without manual reservation management.
Use a site-to-site VPN (AWS Site-to-Site VPN, Azure VPN Gateway, or GCP Cloud VPN) to create a private network tunnel between the cloud instance and the robot cell LAN. On the robot side configure a VPN gateway or use WireGuard on a local edge computer. Set the OPENCLAW_ROBOT_IP environment variable to the robot's LAN address. Round-trip latency over VPN typically adds 5 to 20ms; use the cloud for planning and simulation, the on-premises edge for real-time control.
VMs are simpler and cheaper for single-robot development. Kubernetes (EKS/AKS/GKE) is worth the overhead when managing multiple robot cells, needing automated scaling for burst simulation workloads, or running OpenClaw alongside AI microservices. The openclaw Helm chart supports both single-node and multi-node deployments. GPU node pools in managed Kubernetes services simplify GPU driver management significantly.
Integrate OpenAI GPT-4o, Anthropic Claude 3.5 Sonnet, Google Gemini 2.0 Flash, and self-hosted Ollama LLaMA 3 with OpenClaw for natural-language robot instruction and AI-powered manipulation planning. Includes detailed per-call and monthly cost breakdowns for each AI provider.
Deploy OpenClaw inside Docker containers for reproducible, portable robotics development. This guide covers single-container setups, multi-service docker-compose stacks, GPU passthrough for simulation, and production image hardening.
A step-by-step guide to launching an AI automation agency in 2026, covering niche selection, tool stacks, pricing models, and client acquisition strategies.